Yay Points! Boo Rebounds! What Gets You Played in the NBA?

Editor’s note: All of this is the great work of Ari Caroline, who you’ll note has been added to the about page! I’ve added some minor and organized the charts and tables. In short, any grammatical or commentary errors are my fault! The charts, tables and analysis section are the excellent work of Ari.

The intersection of advanced stats and conventional wisdom.

What makes a good coach? Is it inspiring the troops, grooming young players for succes, keeping locker room harmony? No! It’s playing the players that win. At least, that’s our opinion. However, it turns out things other than winning dictate things like pay, draft position and yes, even playing time in the NBA! Playing time, it turns out, wil be the focus of today’s post. We have a lot to cover. Let’s get started.

What Does it all mean?

We’ve got a lot of data to give you and sometimes that can be hard to understand. Luckily Ari has prepared a handy lookup table you can use!

Term

Plain English

Visual Explanation

Value Explanation

Position Breakdown for WP48

p-value

Is this real??

Is the slope undeniably real? Look at the dark grey bands around the trend line (confidence of fit). Can you draw a flat line through the grey from one side to the other? If so, it’s not very convincing.

Traditional approach looks for values< 0.05 In reality, depending on the number of variables, we usually look for a value < 0.01

C: Weak
PG: Strong!!PF: Weak!!SG: MiddleSF: Strong

R2

How much of the story does it tell?

How tightly are the dots bunched around the trend line? A quick approximation is the light grey bands (confidence of prediction) and the RMSE measure in the upper-right corner (lower is better).

There are no strict criteria for this. Even a low R2 can be very real, just a small part of the story. That said, if you have an overall R2> 50%, you’re explaining a fair bit.

C: Weak
PG: Strong
PF: Weak!!
SG: Middle
SF: Middle

Slope Coefficient

How dramatic is the effect?

How steep is the slope? A steeper slope implies a stronger impact. Finance folks will recognize this as the beta.

Caution: This is easily manipulated by changing the scale. Numbers are meaningless without the context.

C: MiddlePG: Steep!!PF: MiddleSG: SteepSF: Steep

Ok, now let’s hop into the data!

What Gets You Played? The Cold Hard Numbers

2012 NBA Data: What Explains Playing Time?

Position

Predictor

p-value

Incremental R2

Total R2

Centers

PTS48

<0.001

42.1%

55.0%

AST48

0.003

8.0%

WP48

0.012

4.9%

Point Guards

WP48

<0.001

45.0%

54.0%

PTS48

<0.001

9.0%

Power Forwards

PTS48

<0.001

40.2%

46.3%

AST48

0.005

6.1%

Shooting Guards

PTS48

<0.001

36.9%

44.7%

WP48

0.003

7.8%

Swing Forwards

PTS48

<0.001

36.4%

43.0%

WP48

0.009

6.6%

For those more visually inclined, here’s the same data in graphical format.

We stated before that WP48 is far more predictive of playing time for some positions than others

Exhibit #1: MpG vs WP48 (Position Comparison)

Clearly the relationship between WP48 and playing time is very convincing for Point Guards, less so for Shooting Guards, Swing Forwards and Centers and completely unconvincing for Power Forwards

If the value that coaches place on overall productivity (as measured by WP48) in allocating minutes is limited, what do they, in fact, truly value?

Duh, Points!: Exhibit #2: MpG vs PTS48 (Position Comparison)

Using the primer above, we see a convincing relationship for all 5 positions

There is a clearly significant relationship for 4 out of the 5 positions, including Centers and Power Forwards!

Even though Centers only vary from about 0-5 AST48, it seems to effect their playing time significantly. Joakim Noah redemption!?

It’s really sad that Shooting Guards are the exception here.

Boo Rebounds: Exhibit #4: MpG vs. REB48 (Position Comparison)

Only position that is even close to significant (but still not) here is Center

Not Power Forward (this makes no sense at all)

Or any of the other positions (predictable, but still sad)

One could argue that Rebounds are included in the value placed in overall productivity (WP48). Still, that is pretty minimal from an overall weight perspective since WP48 is secondary (or tertiary) for all but PGs and the algorithm includes so many other stats as well.

Notes for Nerds

I did look at some interactions and transformations, primarily to see if I could find anything that made Rebounds significant. Nothing doing.

The interaction of PTS48*AST48 actually supplanted the individual PTS48 and AST48 variables for Centers and PFs and increased the R2 a bit. However, I decided to leave it out for the sake of simplicity.

Next Steps: Discuss the implications of this analysis for potential lineup choices and a really cool experiment that I’m trying to get my 8th grade daughter to do for the CT State Science Fair.

13 Responses to "Yay Points! Boo Rebounds! What Gets You Played in the NBA?"

where a is a constant term and e being the error term. How did you deal with position in the regression? Did you use a dummy variable with one of the positions as the omitted group?

Also when you added interaction terms did you include one for the product of position and each independent variable? What was the logic behind PTS*AST? I understand they explained mpg more so than rpg but why take this approach? I wonder what would be the marginal effects of REB and PTS if you included REB*PTS since everyone raves about double-double forwards and centers.

Always enjoy your posts and thanks for taking the time to answer some of my questions. I’m totally trying to work on something similar and any input would be really helpful in generating some ideas for my regressions.

This is astonishing. There is at least a positive correlation between rebounds and MPG even if it isn’t “statistically significant” – so that is something I guess. I am finding it extremely difficult to think of ways to defend the NBA on this – anyone?

I also do not think that you could argue that Rebounds are included in the value placed in overall productivity (WP48) because when you isolate the rebounds from everything else, there is no significance. It is more likely to be something else in WP48 than it is to be rebounds.

Thanks for the thoughtful comments and questions. I’ll take the risk of scaring everyone else away by answering your questions in technical terms.

I ran this a stepwise regression using, separately, Minimum BIC and a p-value threshold. The resulting equation differed for each position as I ran separate regressions rather than including the position as a dummy variable. This means, of course, that I didn’t look at separate interactions with the position.

I did look at all possible combos of the other four variables. I have no explanation for PTS*AST, but the numbers rarely lie. I’d love to hear any speculation.

@xkonk1 I’d love to compare notes. Unfortunately, I was not working with the full data set available to some of my WoW (are we allowed to steal this from the video game?) colleagues. As that becomes available to me, I plan to supplement the analysis.

people value points WAAY too much. the olympic team that lost shows us this. everyone said “HOW COULD WE LOSE” it was pretty simple, you took every nba player over the past decade that had a low WP but high ppg and threw them on one team and that was the result.

players with high WP like landry fields got their playing time shortened so players with low WP but high scoring would get in like JR smith.
when i asked any NYK fan what they think about landry fields they would say “HE IS GARBAGE HE CANT SCORE!!” but when asked about jr smith they say “OMG HE CAN SCORE HES GOOD”
a 39% shooter is better than a 48% shooter? im confused.
but then you look at box scores, fields only takes good shots and passes when its a hard one so his box scores would read 4-8. 5 reb 4 assist. 9pts
smith does nothing but shoot so his box score would be 6-16 1reb 0assist 14pts… casual fans opinion? omg he SCORES more, he is better!!!

[...] using game theory when mixed with lineups. We’ll probably chat a little bit about his work on what gets players paid and paid in the NBA as well. It’s also been a hectic week in the NBA so we may branch out into other subjects like Mike [...]